In mechanical systems, bearings play a vital role, yet their surfaces frequently develop defects during the production process. Hence, automated detection of such defects becomes indispensable for ensuring industrial quality control. To enhance the robustness of bearing surface defect detection, a dynamic sample matching detection network is proposed, which is guided by multi-level perceptual features. A lightweight adaptive feature extraction method is introduced to enhance defect representation while maintaining low parameter complexity and computational cost. To address diverse defect patterns, a multi-shape perception module with a dual-axis cross-weighting mechanism across multiple scales is designed to improve sensitivity to different defect types. Furthermore, a dynamic sample selection strategy with dual-label weighting is introduced to improve training sample quality. The proposed approach is evaluated on a self-developed bearing defect platform, where it outperforms mainstream detection models in complex environments and achieves superior performance and robustness.